metadata
license: mit
license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE
language:
- en
widget:
- text: Hello who are you?
example_title: Identity
- text: What can you do?
example_title: Capabilities
- text: Create a fastapi endpoint to retrieve the weather given a zip code.
example_title: Coding
tags:
- convAI
- conversational
- TensorBlock
- GGUF
pipeline_tag: text-generation
base_model: abacaj/phi-2-super
model-index:
- name: phi-2-super
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: Instruction Following Eval
type: wis-k/instruction-following-eval
metrics:
- type: acc
value: 0.2717
name: prompt_level_loose_acc
source:
url: https://github.com/huggingface/lighteval
name: LightEval
abacaj/phi-2-super - GGUF
This repo contains GGUF format model files for abacaj/phi-2-super.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.
Our projects
| Forge | |
|---|---|
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| An OpenAI-compatible multi-provider routing layer. | |
| 🚀 Try it now! 🚀 | |
| Awesome MCP Servers | TensorBlock Studio |
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| A comprehensive collection of Model Context Protocol (MCP) servers. | A lightweight, open, and extensible multi-LLM interaction studio. |
| 👀 See what we built 👀 | 👀 See what we built 👀 |
<|endoftext|>[INST] {prompt} [/INST]
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| phi-2-super-Q2_K.gguf | Q2_K | 1.110 GB | smallest, significant quality loss - not recommended for most purposes |
| phi-2-super-Q3_K_S.gguf | Q3_K_S | 1.251 GB | very small, high quality loss |
| phi-2-super-Q3_K_M.gguf | Q3_K_M | 1.426 GB | very small, high quality loss |
| phi-2-super-Q3_K_L.gguf | Q3_K_L | 1.575 GB | small, substantial quality loss |
| phi-2-super-Q4_0.gguf | Q4_0 | 1.602 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| phi-2-super-Q4_K_S.gguf | Q4_K_S | 1.619 GB | small, greater quality loss |
| phi-2-super-Q4_K_M.gguf | Q4_K_M | 1.738 GB | medium, balanced quality - recommended |
| phi-2-super-Q5_0.gguf | Q5_0 | 1.933 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| phi-2-super-Q5_K_S.gguf | Q5_K_S | 1.933 GB | large, low quality loss - recommended |
| phi-2-super-Q5_K_M.gguf | Q5_K_M | 2.003 GB | large, very low quality loss - recommended |
| phi-2-super-Q6_K.gguf | Q6_K | 2.285 GB | very large, extremely low quality loss |
| phi-2-super-Q8_0.gguf | Q8_0 | 2.958 GB | very large, extremely low quality loss - not recommended |
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/phi-2-super-GGUF --include "phi-2-super-Q2_K.gguf" --local-dir MY_LOCAL_DIR
If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:
huggingface-cli download tensorblock/phi-2-super-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'